This is an open access article distributed under the terms of the Creative Commons Attribution License (
http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Methods

Peripheral blood leukocytes were collected from 65 healthy donors and 103 SLE patients,
60 of whom had samples from 2 or more visits. Total RNA was isolated and analyzed
for the expression of mRNA and microRNA using Taqman real time PCR assays. Relative
expression of I-IFN signature genes, chemokines, and miR-146a were determined by the
ΔΔCT method. Results were correlated with clinical data and analyzed by Wilcoxon/Kruskal-Wallis
test and Fisher’s exact test.

Results

Levels of ADAR, CCL2, CXCL10, and STAT1 in SLE were significantly elevated compared
with the healthy controls (P <0.0001). ADAR, CCL2, and CXCL10 showed significant correlation with IFN score in
both healthy donors (P <0.0033) and SLE patients (P <0.0001). In SLE patients, miR-146a level was not significantly different from healthy
controls nor correlated to the IFN score. Two STAT1 populations were identified: a
low STAT1 and a high STAT1 group. High STAT1 patient visits displayed higher (P ≤0.0020) levels of CCL2 and CXCL10 than the low STAT1 patient visits. STAT1 levels
correlated with IFN score in low STAT1 group but not in high STAT1 group. More importantly,
high STAT1 levels appeared as an enhancer of CCL2 and CXCL10 as indicated by the significantly
stronger correlation of CCL2 and CXCL10 with IFN score in high STAT1 patient visits
relative to low STAT1 patient visits.

Conclusion

Our data indicate a novel role for STAT1 in the pathogenesis of SLE as an expression
enhancer of CCL2 and CXCL10 in SLE patients with high levels of STAT1. Future study
is needed to examine the exact role of STAT1 in the etiology of SLE.

Introduction

Systemic lupus erythematosus (SLE) is a chronic systemic autoimmune disease characterized
by periods of increased disease activity, referred to as flare-ups, and periods of
remission. Several genetic and environmental factors have been implicated in SLE etiopathogenesis,
but in recent years increased type I interferon (IFN-I, IFNα and IFNβ) expression
has been discovered to play a key role in the majority of SLE patients, despite being
known for over 30 years that it is elevated in SLE patients
[1-4]. Because of the technical challenges in measuring the numerous isoforms of IFNα,
one common way to evaluate IFN-I expression is to examine the levels of common IFN-inducible
genes, such as 2′,5′-oligoadenylate synthetase (OAS1), myxovirus resistance 1 (MX1),
and lymphocyte antigen 6 complex locus E (LY6E); the mRNA levels of these IFN-I-inducible
genes are then used to calculate the IFN score
[1,5-7]. Another interferon inducible gene that plays an important antiviral and immunomodulatory
function is the adenosine deaminase acting on RNA (ADAR). ADAR is an enzyme that catalyzes
the conversion from adenosine (A) to inosine (I) in double-stranded RNA (dsRNA) substrate
[8,9], with an impact on RNA at different levels, such as mRNA splicing and degradation
[10,11]. Furthermore, ADAR1 has been observed to suppress interferon regulatory factor (IRF)3
and protein kinase RNA-activated (PKR) and therefore blocking IFN induction
[12-14]. The ability of ADAR1 to respond and regulate IFN-I production makes it an intriguing
IFN-I-inducible gene to examine in SLE. Up to now, ADAR1 expression has only been
observed in T-cells of SLE patients, as shown in a limited number of studies
[15-17]. In fact, Laxminarayana et al. showed that ADAR1 is upregulated approximately 3-fold in SLE patients
[15]. The same group later observed the increased editing of ADAR2 by ADAR1 in T-cells
of SLE patients
[16]. Additionally, due to increased ADAR1 in SLE patients, Orlowski et al. observed an increase of phosphodiesterase 8A1, which participates in the termination
of cyclic nucleotide signaling by hydrolyzing cAMP and cGMP and is activated by IFN
and enhances T-cell adhesion
[17].

Other IFN-I-inducible genes include signal transducers and activators of transcription
(STAT)1 and 2. STAT1 is involved in type I, II, and III IFN signaling and has been
observed to be elevated in SLE
[18]. In response to type I IFN, STAT1 causes IFN receptor (IFNAR)1 and 2 dimerization,
activation and phosphorylation of IFNAR by Tyk2 and Jak1, and thus docking and phosphorylation
of STAT1 and STAT2
[19]. The heterodimer STAT1-STAT2 is then translocated into the nucleus where it can bind
specific promoters playing a key role in IFN signaling and production
[20].

Besides STAT1 and ADAR, IFN-regulated chemokines have become another important topic
of research in recent years
[21]. Two of these chemokines have been shown to be SLE biomarkers, and they are called
C-C motif chemokine ligand 2 (CCL2) and C-X-C motif chemokine 10 (CXCL10)
[22]. CCL2, formerly referred to as monocyte chemotactic protein-1 (MCP-1), is a potent
recruiter of monocytes, T-cells, basophils, and dendritic cells to the site of infection
or tissue damage, but it has no effect on neutrophils or eosinophils unless the N-terminus
of CCL2 is cleaved
[18,23]. Some cell types such as monocytes, macrophages, and dendritic cells can primarily
secrete CCL2, which signals via the cell surface receptors CCR2 and CCR4 and is upregulated
by IFNα and IFNβ
[24,25]. The role of CCL2 is beneficial in clearing pathogens, but it has also been involved
in some pathological processes. In a glomerulonephritis mouse model, CCL2 plays a
role in crescent formation and interstitial fibrosis supported by the observation
that anti-CCL2 antibodies can reduce crescent formation, renal impairment, and scarring,
as well as T cell and macrophage infiltration
[26]. CCL2 has been observed in the recruitment of T cells and monocytes/macrophages in
lupus nephritis and blockade of CCL2 ameliorates lupus nephritis in MRL-(Fas)lpr mice
[23,27]. In a serologic proteome study by antibody microarray in SLE, CCL2 was identified
as one of the twelve upregulated proteins; furthermore CCL2 was one of three chemokines
that would precede lupus flare, indicating that they are good predictors of increased
SLE activity
[21].

CXCL10, also known as IFN gamma-induced protein 10 (IP-10), is a chemokine of the
C-X-C motif family. Similar to CCL2, CXCL10 is a potent attractor of monocytes, macrophages,
T-cells, natural killer (NK) cells, and dendritic cells to sites of tissue damage
and infection
[28,29]. CXCL10 is an IFN-response cytokine that binds its CCL3 receptor and acts via Jak/STAT
pathway activation
[30-32]. Even though CXCL10 is a potent immune responder for bacterial and viral infections
and a critical biomarker for organ transplant rejection, its role in the pathogenesis
of autoimmune diseases is not clear
[33,34]. Furthermore, the combination of CXCL10 and CCL2 protein levels could be useful as
prediction factor for upcoming flares
[22].

The reason behind upregulation and control of IFN in SLE is not known, but some studies
have recently focused on the possible role played by selected microRNAs (miRNAs).
MiRNAs are small non-encoding 20- to 23-nucleotide-long RNAs, that regulate their
target mRNA by binding to the 3′ UTR, causing translational repression and/or degradation
of targets. miR-146a is one of the most significant miRNAs in regulating innate immune
response and tolerance
[35] and it was first shown to be involved in toll-like receptor (TLR) regulation through
the nuclear factor (NF)-кB pathway
[36]. miR-146a would function to attenuate the immune response and regulate inflammation
in normal immune response and autoimmune disorders, and it is also a critical regulator
of endotoxin-induced tolerance and cross-tolerance
[37-39]. To date, miR-146a has been found in association with autoimmune diseases such as
Sjögren’s syndrome
[40], psoriasis
[41,42], and rheumatoid arthritis
[43-45].

Tang et al. reported that miR-146a was under-expressed in peripheral blood mononuclear cells
(PBMCs) of Chinese SLE patients
[46]. miR-146a was significantly lower in patients with active SLE with proteinuria compared
to those with inactive SLE
[46]. Additionally, SLE patients displayed an inverse correlation between miR-146a expression
and IFN score
[46]. Tang et al. also demonstrated that reduction of miR-146a may enhance the signaling due to elevated
levels of STAT1 and IRF5 which leads to increased production of IFN
[46]. The reduced levels of miR-146a observed in Chinese SLE patients could potentially
explain elevation of IFN by loss of regulation of STAT1 expression.

Our present study evaluates the interaction among STAT1, ADAR, CCL2, CXCL10, and miR-146a
in SLE patients and healthy controls, demonstrating that all except for miR-146a correlate
with IFN score in both SLE patients and healthy donors.

Methods

Healthy donors’ and SLE patients’ demographic data

Whole blood was collected from a total of 103 SLE patients and 65 healthy controls
enrolled in the University of Florida Center for Autoimmune Diseases registry from
2008 to 2011. Healthy donors (HD) were selected based on no history of autoimmune
disease, and all SLE patients satisfied the American College of Rheumatology (ACR)
criteria
[47]. Healthy donors only visited the clinic once, therefore, they represent a single
visit. There were a total of 180 SLE visits with sequential samples collected in 60
SLE patients (Table
1). SLE patients and healthy controls were segregated by ethnic profile (Table
1). All human blood samples were obtained from enrolled individuals with the approval
of the institutional review board (IRB) at the University of Florida. This study meets
and is in compliance with all ethical standards in medicine and informed consent was
obtained from all patients according to the Declaration of Helsinki.

Leukocytes and RNA purification

Peripheral blood leukocytes were collected from whole blood using Ambion LeukoLOCK
kit (Ambion, Austin, TX, USA). LeukoLOCK filters were washed twice with 3 ml of PBS
and stabilized with 3 ml of RNAlater solution. Stabilized filters were stored for
a minimum of 24 h at −80°C before collecting total RNA. Total RNA, including small
RNAs, was collected using the “Alternative Protocol” (version 0602, Ambion) for the
extraction of RNA from cells captured on LeukoLOCK filters using TRI reagent.

Anti-dsDNA ELISA

After the collection of leukocytes with the LeukoLOCK filters, the leukocyte free
blood was transferred to 10 ml Vacutainer SST plus blood-collection tubes (BD, Franklin
Lakes, NJ, USA). Blood was centrifuged at 1,000 g for 20 minutes. The plasma was transferred
to a 15-ml conical tube and stored at −20°C. Anti-dsDNA ELISA was performed as previously
described
[48]. In brief, anti-human IgG secondary antibody was used and samples were considered
positive when the absorbance was greater than the mean plus three SD from the healthy
controls.

Complement levels

C3 and C4 complement levels were obtained from clinical data. C3 levels lower than
90 mg/dl and C4 levels less than 15 mg/dl were considered as low complement levels
in the analysis.

IFN score and SLE activity

The expression of three known type-I IFN signature genes, MX1, OAS1, and LY6E, was
z-transformed into IFN score as previously shown
[1,49]. The SLE disease activity index (SLEDAI) was used to classify the patients into active
(SLEDAI >4) or inactive (SLEDAI ≤4) at the time of the visit (Table
1)
[50,51].

Data analysis

The copy number of miR-146a was normalized to total loaded RNA, whereas mRNA levels
were normalized to 18S RNA. The copy number of miR-146a was determined using a standard
curve with synthetic miR-146a (Integrated DNA Technologies Inc., Coralville, IA, USA)
[52]. Relative expression of mRNA compared to controls was determined by the ΔΔCT (cycle threshold) method
[53]. Analyses were performed using SAS version 9.2 and JMP Genomics version 5 (SAS, Cary,
NC, USA). The Wilcoxon/Kruskal-Wallis test was used to evaluate significance between
groups, whereas the Wilcoxon signed rank test for matched pairs was used to evaluate
SLE patients with two visits. P-values <0.05 were considered significant. Before applying ordinary linear regression
analyses, the distributions of datasets were confirmed for normality. The coefficient
of determination (r2) was used to determine linear correlation. Significant differences between slopes
was evaluated by analysis of covariance (ANCOVA). The Generalized Estimating Equation
(GEE) model for Repeated Measures was used to account for possible with-in subject
effects from patients with multiple visits
[54].

Results

Expression of candidate biomarkers in the SLE cohort

To determine whether previously reported biomarkers were elevated in our SLE patient
cohort, we measured the biomarker expression levels in HD, active SLE, and inactive
SLE patient visits (Figure
1). The SLE cohort was segregated by SLEDAI into active SLE (49 visits, SLEDAI >4)
and inactive SLE (131 visits, SLEDAI ≤4). The level of IFN-I was estimated by quantifying
the expression of IFN-inducible genes. The IFN score, STAT1, ADAR, CCL2, and CXCL10
levels were significantly elevated at both active and inactive SLE patient visits
compared to HD (Figure
1A-E), establishing and confirming that these biomarkers were aberrantly overexpressed
in our SLE patients. To explore if these biomarkers were capable of distinguishing
disease activity status, active and inactive patient visits were compared to one another.
No significant difference was observed between active and inactive SLE patient visits
for IFN score (Figure
1A, mean ± SD, 62.7 ± 6.1 units versus 57.8 ± 4.9 units), ADAR (Figure
1C, 5.27 ± 0.31 fold versus 5.27 ± 0.23 fold), and CXCL10 (Figure
1E, 158.1-fold ± 26.6 versus 120.0-fold ± 10.5), but STAT1 (Figure
1B, 44.8 ± 10.7 vs 34.4 ± 6.6 fold, P = 0.033) and CCL2 (Figure
1D, 18.2-fold ± 3.1 versus 9.96-fold ± 1.42, P = 0.0061) were significantly elevated in active SLE compared to inactive SLE patient
visits. TNFα, which is not generally involved in the pathogenesis of SLE, was used
as a negative control. As expected, TNFα was not significantly different among the
three groups (Figure
1F). Similarly miR-146a did not display any significant difference among active SLE,
inactive SLE, and HD (Figure
1G). To validate this, we determined the levels of the primary transcript of miR-146a
(pri-mir-146a) which also did not demonstrate any significant difference among active
SLE, inactive SLE, and HD. With the exception of miR-146a, these results are consistent
with reports on SLE patients with elevated IFN score compared to HD
[1,49] as well as upregulated levels of IFN signature genes (STAT1 and ADAR)
[15-17] and chemokines (CCL2 and CXCL10)
[21].

The clinical and expression data were correlated with anti-dsDNA autoantibody level,
which is an indicator for patients’ disease activity in certain patients
[55-58]. Decreases in C3 and C4 levels correlated with SLE activity and renal damage as well
as increased levels of anti-dsDNA antibodies
[59]. Anti-dsDNA autoantibody levels have also been used for sub-classification of SLE
patients
[60,61]. SLE patient visits and HD were segregated into anti-dsDNA(+) and anti-dsDNA(−).
Patient visits that were anti-dsDNA(+) displayed higher SLEDAI and decreased C3 and
C4 levels (Figure
2A-C). The results for the remaining biomarkers (Figure
2D-K) closely resembled those from active versus inactive SLEDAI results (Figure
1).

The influence of race in anti-dsDNA, IFN score, STAT1, CCL2, and CXCL10 were also
examined. African Americans (AA) and European Americans (EA) contributed to 83.3%
of the visits, followed by Latin Americans (LA) and Asian Americans (AsA) for 15%,
and interracial Americans (IrA) for less than 2% of patient visits (Table
1). Due to the small sample size, IrA were excluded in all subsequent analyses. In
general, results show that higher levels of anti-dsDNA, IFN score, STAT1, CCL2, and
CXCL10 were observed in all race groups analyzed (Additional file
1: Figure S1). The lack of statistically significant differences between SLE and HD
in certain groups, such as LA, might be due to the smaller sample sizes.

Biomarker interrelationship in SLE patients with return visits

To expand upon the interrelationship of these biomarkers, data from SLE patients with
two consecutive visits were segregated for analyses by increasing or decreasing IFN
score by at least 50% from the first to the second visit. Patients with increasing
IFN score from one visit to the next (n =13; P = 0.0001, Figure
3A) displayed a significant increase in STAT1 (P = 0.0017), CCL2 (P = 0.0086), CXCL10 (P = 0.038), and miR-146a (P = 0.0034). Similarly, for SLE patients with increasing STAT1 by at least 50% between
the first and second visit (n = 25; P <0.0001, Figure
3B), significant increases were observed for IFN score (P = 0.027), CCL2 (P <0.0001), CXCL10 (P = 0.0003), and miR-146a (P = 0.0078). The strong correlation between STAT1, CCL2, and CXCL10 were expected;
however, correlation between IFN score and increasing STAT1 was weaker than expected.
This may be indicating that high STAT1 levels do not necessarily translate into high
levels of IFN-I. The highly significant correlation between miR-146a levels and IFN
score in the return visits was unexpected, as the level of miR-146a in SLE was not
significantly different from HD (Figures
1H and
2H) and also it was previously reported to be decreased in SLE and inversely correlated
with IFN score in a Chinese SLE cohort
[46].

Figure 3.Systemic lupus erythematosus (SLE) patients with two visits ranked by increasing or
decreasing IFN score and STAT1. Data from the first and second visits for each patient is denoted by an individual
color line. (A) SLE patients ranked by increasing IFN score from the first to the second visit showed
significant increase in STAT1, CCL2, CXCL10, and miR-146a. (B) Patients ranked by increasing STAT1 also showed significant increase in IFN score,
CCL2, CXCL10, and miR-146a. (C) SLE patients ranked by decreasing IFN score from the first to the second visit showed
significant decrease only in STAT1 and CXCL10. (D) Patients ranked by decreasing STAT1 showed significant decrease in IFN score and
CXCL10. STAT, signal transducers and activators of transcription; CCL2, C-C motif
chemokine ligand 2; CXCL10, C-X-C motif chemokine 10.

SLE patients who had decreasing IFN score by at least 50% between first and second
visit (N = 32; P <0.0001, Figure
3C) displayed a significant decrease in STAT1 (P = 0.0002) and CXCL10 (P = 0.0002), but not in CCL2 and miR-146a. Similarly, SLE patients with decreasing
STAT1 (n = 13; P = 0.0001, Figure
3D) had significant decrease in IFN score (P = 0.0001) and CXCL10 (P = 0.0004), whereas no significant changes in CCL2 and miR-146a were observed. By
ranking patients according to decreasing IFN score or STAT1, the reversal of the results
from ranking by increasing IFN score or STAT1 should ideally have been observed. Interestingly,
the exception was observed only for CCL2 and miR-146a (Figure
3C, D).

Relationship of IFN score to other biomarkers

To better understand whether the association of IFN score with the other biomarkers
in paired patient-visits could be expanded, levels of ADAR, CCL2, and CXCL10 from
the entire cohort of SLE patient visits and HD were correlated to IFN score (Figure
4). ADAR, CCL2, and CXCL10 displayed significant coefficient of determination (r2) in both SLE and HD (Figure
4). The consistent significant correlations of these genes to IFN from the low levels
observed in HD (Figure
4, right panels) to aberrantly high pathogenic levels of IFN in SLE patient visits
(Figure
4, left panels) was indicative of a normal intrinsic response of ADAR, CCL2, and CXCL10
to IFN production. Contrary to an earlier report showing that the level of miR-146a
was negatively correlated with IFN score
[46], miR-146a as well as pri-miR-146a did not display any significant correlation with
IFN score in either HD or SLE patients (data not shown). Surprisingly, in the same
type of analysis, STAT1 did not display a significant correlation to IFN score either
(data not shown). Further analysis of STAT1 expression revealed two populations after
applying a log10 transformation (Log[STAT1]) in both HD and SLE patients (Figure
5A). Using an arbitrary cut off of 1.50 Log[STAT1] to segregate STAT1 results, values
below 1.50 were referred as the low-STAT1 group and above 1.50 were the high-STAT1
group (Figure
5B, C). In the low STAT1 group, SLE patient visits displayed significantly higher expression
of STAT1 compared to HD (2.44-fold, P <0.0001, Figure
5B), but in the high-STAT1 group, no significant difference was observed (Figure
5C). Furthermore, the low-STAT1 group displayed significant positive association between
STAT1 and IFN score in both HD (Figure
5D) and SLE patients (Figure
5E). In contrast, in the high-STAT1 group there was no correlation between STAT1 and
IFN score (data not shown).

Figure 5.Bimodal distribution of STAT1 into high and low groups. (A) The log10 transformation of STAT1 shows a bimodal distribution of STAT1 with two populations
(high- and low-STAT1 groups) with a cut off at 1.5 log[relative fold-change] of STAT1
(Log[STAT1]) for both healthy donor (HD) and SLE patient visits. (B) The low-STAT1 groups displayed significant difference of STAT1 in SLE patient visits
compared to HD. (C) On the other hand, the high-STAT1 groups showed no significant difference between
SLE and HD. (D, E) In the low-STAT1 group, STAT1 levels display a direct correlation to the IFN score
in SLE patient visits and HD. STAT, signal transducers and activators of transcription.

STAT1 levels correlate with SLE activity

The effects of high and low STAT1 on IFN score and ADAR appeared to be related to
the active versus inactive status of SLE (SLEDAI, Figure
1A, C) and anti-dsDNA(+) versus (−) patients (Figure
2A, C) where IFN score and ADAR were significantly higher than in HD, but not significantly
different between SLE patient visits with high and low STAT1 (Figure
6A, B). CCL2 was significantly different between active and inactive SLE, and between
HD and active and inactive SLE as well (Figure
1D), which resembles the results of anti-dsDNA (+ versus -) (Figure
2D) and high- versus low-STAT1 comparisons (Figure
6D). Similar observations are valid for CCL2, with the addition that there is a difference
in CCL2 expression between high- and low-STAT1 SLE (Figure
6C). As both SLEDAI active and anti-dsDNA (+) are indicators of increased disease activity,
these results indicate that patients with high STAT1 are also in a more active disease
state than those with low STAT1.

To determine whether ethnicity could be a confounding factor for the effects of high
and low STAT1, IFN score, CCL2, and CXCL10 levels were segregated based on ethnicity
and high and low STAT1 (Additional file
1: Figure S3A-C). Overall, high STAT1 patient visits did not show a significant difference
among AA, EA, and LA. However, low-STAT1 AA patients showed significantly higher IFN
score, CCL2 and CXCL10 compared to other groups (Additional file
1: Figure S3). These results indicated that high- and low-STAT1 groups were identified
essentially in all ethnicities, and differences in IFN score, CCL2, and CXCL2 levels
were observed among low-STAT1 groups but not among the high-STAT1 groups.

STAT1 influences the covariance of IFN score with ADAR, CCL2, and CXCL10

The slope of the linear regression represents the rate of change of ADAR, CCL2, and
CXCL10 per unit of change in IFN score. This led to the intriguing possibility that
patient visits with high STAT1 have a higher slope than those with low STAT1. ANCOVA
was used to test if the slopes were significantly different (Figure
7). ADAR/IFN scores were not significantly different between high- and low-STAT1 patients
(Figure
7A, blue versus red line, P-value not shown), but CCL2/IFN score and CXCL10/IFN score slopes were significantly
higher in the high-STAT1 (HS) patients compared to the low STAT1 (LS) patients (Figure
7B, C, blue versus red line). This suggests that high STAT1 levels may enhance CCL2
and CXCL10 expression potentially induced by IFN.

Induction of STAT1, CCL2, and CXCL10 in THP-1 cells with type I IFN

TLRs have been implicated to play a role in SLE pathogenesis. To model the response
of STAT1, CCL2, and CXCL10 as well as IFN-I, TLR4 was stimulated in human monocytic
THP-1 for 24 h with 1,000 ng/ml of LPS. IFN score increased at around 4 h and peaked
around 8 h (Figure
8A). In 1.0 ng/ml of IFNα2-treated and 0.1 ng/ml of IFNβ-treated THP-1 cells, IFN score
displayed a similar trend as in LPS treatment (Figure
8F, K); however for 1.0 ng/ml IFNβ-treated cells, IFN score increased up till 12 h
(Figure
8K), whereas 0.1 ng/ml-treated cells displayed little change (Figure
8F). These results demonstrated THP-1 responsiveness to IFN-I as well as the fact that
they were capable of IFN-I production.

Figure 8.THP-1 response to IFNα, IFNβ, and LPS over a period of 24 h. THP-1 cells were treated with different doses of IFNα, IFNβ, and LPS and lysates
were harvested at various times from 2 to 24 h for RNA isolation and analyses. IFN
score (A, F, K), and the expression of STAT1 (B, G, L), CCL2 (C, H, M), CXCL10 (D, I, N), and miR-146a (E, J, O) were evaluated at 0.1 and 1.0 ng/ml of IFNα2 and IFNβ as well as 1,000 ng/ml of LPS.
LPS, lipopolysaccharide; STAT, signal transducers and activators of transcription;
CCL2, C-C motif chemokine ligand 2; CXCL10, C-X-C motif chemokine 10; UTX, untreated.

Interestingly, whereas LPS displayed a gradual, long-term increase of CCL2 and CXCL10,
IFNα2 and IFNβ treatments displayed rapid increases followed by decreases of CCL2
and CXCL10. After LPS stimulation, STAT1 did not increase till 4 h and reached its
peak expression at 8 h (Figure
8B); however in THP-1 cells stimulated with IFNα2 or IFNβ, STAT1 increased at 2 h,
peaking at 8 h (Figure
8G, L). CCL2 increased at 2 h in LPS-treated THP-1 cells and continued to increase
during the 24-h period (Figure
8C); however this was not until after maximum expression of STAT1 was reached (Figure
8B), and CCL2 began to rapidly increase (Figure
8C). CCL2 increased at 2 h in 0.1 and 1.0 ng/ml IFNα2-treated as well as 0.1 ng/ml
IFNβ-treated THP-1 cells, but it peaked at 4 h and began to decrease rapidly (Figure
8H). For 1.0 ng/ml IFNβ treatment of THP-1 cells, the peak was shifted by 2 h so that
CCL2 peaked at 2 h and began to rapidly decrease (Figure
8M). CXCL10 displayed a trend similar to CCL2 for 1.0 ng/ml IFNα2-treated and 0.1 ng/ml
IFNβ-treated THP-1 cells (Figure
8I, N). In 1.0 ng/ml IFNβ treatment of THP-1 cells, CXCL10 continued till 8 h (Figure
8N). These results indicated that CCL2 and CXCL10 rapidly responded to IFNα2 and IFNβ
stimulation whereas TLR4 stimulation appeared to induce a slow gradual increase, but
then a rapid increase after STAT1 reached its maximum expression.

miR-146a appeared to differ in its response from the other biomarkers. LPS upregulated
miR-146 3-fold and it rapidly reached a peak of an 11-fold increase at 12 h (Figure
8E). miR-146a in IFNα2- or IFNβ-treated cells showed a modest of 3- to 4-fold peak
at 8 h, potentially indicating that IFN-I did not induce significant production of
miR-146a (Figure
8J, O).

Discussion

In this study, expression of previously identified SLE biomarkers was examined and
correlation tested with demographic and clinical parameters, focusing on the analysis
of a possible correlation among them. The primary analyses used ordinary linear regression,
even for data from multiple visits, as reported in Figures
4,
5, and
7. Alternatively, the GEE model for repeated measures was also used to account for
possible within-subject effects from patients with multiple visits
[54]. When we compared the parameters (slope and significance) from the GEE and ordinary
linear regression, the results were practically identical (data not shown). It is
known that unless the vast majority of the samples have repeated measures (patients
with multiple visits), the ordinary linear regression is expected to closely approximate
the GEE model
[64]. Furthermore, even if there was strong correlation between visits of patients, ordinary
linear regression would underestimate the correlation because it assumes that the
visits are independent; therefore, the correlations of ordinary linear regressions
are more stringent than those of GEE
[64]. In addition, we also assessed the normality of each dataset before applying linear
regression. With the exception of STAT1, the IFN score, ADAR, CCL2, and CXCL10 resembled
normal distributions (data not shown). In most cases when dealing with such large
datasets, even moderate deviations from normalcy are not critical due to the central
limit theorem
[65]. For these reasons, we decided to report ordinary linear regression rather than the
more complex GEE model for repeated samples.

Biomarker assessment

Our results show that ADAR, STAT1, CCL2, and CXCL10 levels were significantly elevated
in the SLE cohort as expected. This is in part validated by previously published results
showing increased levels of these biomarkers and their correlation to IFN-I production
in SLE patients
[1,2,6,21,22,66]. Furthermore, our study shows that THP-1 cells treated with IFNα2 or IFNβ display
up to 18-fold increase of STAT1, 25-fold increase of CCL2, and 700-fold increase of
CXCL10, confirming that these genes respond to IFN-I stimulation.

Tang et al. reported miR-146a under-expression in SLE PBMCs
[46], whereas we did not observe a decrease or a difference between patients with active
or inactive SLE for miR-146a expression in peripheral blood leukocytes of SLE patients
in our cohort. Luo et al.
[67] hypothesize that a functional variant in the miR-146a promoter may be responsible
for decreased levels of miR-146a in SLE, so the pri-miR-146a levels should be decreased
in our population; however, no significant differences in pri-miR-146a expression
were observed in our population. Furthermore, Tang et al. reported inverse correlation between miR-146a and IFN score in their SLE cohort,
while we did not observe a significant correlation in our cohort. A significant increase
in miR-146a was observed only in SLE patients with increasing IFN score between the
initial and the second visit
[36]. Other possible explanations for the discrepancy between the two datasets could be
the difference in cell populations and racial composition in our cohort versus the
one examined by Tang et al. and Luo et al.
[46,67]. As for the THP-1 monocyte cell model, IFN-I weakly stimulated miR-146a expression
compared to LPS. All these results suggest that the role of miR-146a in regulating
IFN-I in our cohort of SLE patients may have been limited.

Biomarker connections

Previous reports have demonstrated the involvement of ADAR mRNA and CCL2 and CXCL10
protein in SLE
[15-17,22]. In published literature, ADAR mRNA and CCL2, CXCL10 protein levels displayed a positive
association with IFN score
[11,21,22,68]. Similarly in our cohort, directly correlation between IFN score and mRNA levels
of ADAR, CCL2, and CXCL10 was observed. This was observed not only in SLE but in HD
as well, potentially indicating that these genes are responding normally to IFN even
when at levels aberrantly elevated. Unlike reports from previous studies, STAT1 did
not correlate well with the IFN score in the SLE patient population
[69,70]. Instead, patients with low-STAT1 SLE and HD with low-STAT1, the expression was associated
with IFN score. Patients paired by two visits that were ranked by increasing IFN score
demonstrate strong covariance with STAT1, but the covariance between IFN score and
increasing STAT1 appeared to be weaker. In paired SLE patient visits, decreasing IFN
scores or STAT1 level is accompanied by a decrease of the other biomarkers suggesting
that STAT1 and IFN-I may be driving factors.

When SLE patient visits are grouped into high and low STAT1, high-STAT1 SLE patient
visits showed significantly higher levels of CCL2 and CXCL10. After grouping by high
and low STAT1, the high-STAT1 patient visits showed a significantly increased slope
for CCL2/IFN and CXCL10/IFN scores compared to low-STAT1 SLE patient visits. This
enhanced response by CCL2 and CXCL10 to IFN-I in high-STAT1 patients may be due in
part to the role of STAT1 in activation of CCL2 and CXCL10
[71-73]. Hence, STAT1 levels appear to be enhancing chemokine response to IFN-I.

Furthermore, THP-1 cells treated with IFNα2, IFNβ, or even LPS, demonstrated that
IFN score, STAT1, CCL2, CXCL10 and miR-146a were upregulated in a time-dependent manner.
IFNα2 or IFNβ treatment of THP-1 cells shows that cells expressed decreased levels
of CCL2 and CXCL10 shortly after reaching their peak expression, whereas LPS treatment
displayed a steady increase of CCL2 and CXCL10 with a less rapid induction compared
to their expression after IFNα2 or IFNβ stimulation. After STAT1 peak expression in
LPS-treated THP-1 cells, CCL2 and CXCL10 expression rapidly accelerated. On the contrary,
IFNα2 and IFNβ treatment of THP-1 cells shows that CCL2 and CXCL10 both started decreasing
after reaching their peak expression, whereas STAT1 continued to increase with IFNα2
or IFNβ stimulation. These results indicate that CCL2 and CXCL10 respond differently
to TLR4 stimulation compared to IFN signaling. It also indicates that CCL2 and CXCL10
response to IFN-I is rapid but short compared to TLR signaling, as IFN score correlates
with greater increase of CCL2 and CXCL10 in the high-STAT1 patients than in low-STAT1
patients. The results of TLR4 stimulation suggest that at least in the high-STAT1
patient population CCL2 and CXCL10 are being driven by TLR signaling rather than IFN-I
directly since IFN-I stimulation caused a rapid increase followed by an equally rapid
decrease of CCL2 and CXCL10 independent of STAT1 expression.

It is unclear why STAT1 was elevated to such high levels in some of the SLE patients
and HD. One possibility is from TLR activation as seen in the LPS stimulations. Another
possibility is impairment in the expression of miR-146a, which is known to target
STAT1
[46]. In the paired SLE-patient visits, miR-146a might be increased as a response to STAT1
increases, but it is unable to downregulate STAT1. One potential reason that miR-146a
is unable to downregulate STAT1 is due to alternative splicing. STAT1 exists as a
long form (STAT1a) and short form (STAT1b). According to the miRNA target prediction
site, TargetScan.com, STAT1b has a shorter 3′ UTR compared to STAT1a 3′ UTR. The shorter
3′ UTR in STAT1b lacks miR-146a binding sites, which would prevent miR-146a downregulation
of STAT1b. Several HD also displayed very high STAT1 levels, however CCL2 and CXCL10,
even though elevated compared to low-STAT1 HD, were significantly lower than in SLE
patients. A potential reason is that IFN-I drives CCL2 and CXCL10 expression, and
high STAT1 primes the immune system to amplify CCL2 and CXCL10 expression when IFN-I
is present. Without IFN-I, the high STAT1 levels may still prime the immune system
but they lack the ignition to drive the process forward.

Conclusions

The results of this study show that STAT1 mRNA expression in PBMCs from lupus patients
and healthy controls is segregated into high- or low-STAT1 groups. STAT1 may be an
important driver of lupus pathogenesis with STAT1 serving as an expression enhancer
of CCL2 and CXCL10 in patients with high levels of STAT1.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

PRDG carried out the experiments. PRDG, MS and EKLC designed the study. PRDG, AC,
and MS performed the statistical analysis. ESS, AC, and WHR enrolled patients for
the study, collected information and maintained the database. PRDG, AC, and EKLC drafted
the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Supported in part by a grant from the Lupus Research Institute and the National Institutes
of Health grant Al47859. PRDG was supported by NIH training grant T90/R90 DE007200.
We thank all the staff at the Division of Rheumatology for collection of blood and
clinical information.